CN107894827A

CN107894827A - Using method for cleaning, device, storage medium and electronic equipment

Info

Publication number: CN107894827A
Application number: CN201711050143.3A
Authority: CN
Inventors: 梁昆
Original assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Current assignee: Guangdong Oppo Mobile Telecommunications Corp Ltd
Priority date: 2017-10-31
Filing date: 2017-10-31
Publication date: 2018-04-10
Anticipated expiration: 2037-10-31
Also published as: CN107894827B

Abstract

The embodiment of the present application discloses one kind and applies method for cleaning, device, storage medium and electronic equipment, wherein, this method includes：The multidimensional characteristic of continuous acquisition application builds multiple sample sets of application as sample；The sample set of predetermined number is extracted from multiple sample sets；Sample set based on predetermined number, sample classification is carried out to each sample set according to information gain of the feature for sample classification successively, to construct multiple decision-tree models of application, the output result of decision-tree model includes to clear up or can not clearing up；The multidimensional characteristic of current time application is gathered as forecast sample；Whether judge to apply according to forecast sample and multiple decision-tree models can clear up.With multiple decision-tree models synthesis using cleaning judge, to clear up the application that can be cleared up, realize the higher automatic cleaning of accuracy, improve the speed of service of electronic equipment, and reduce power.

Description

Using method for cleaning, device, storage medium and electronic equipment

Technical field

The application is related to communication technical field, and in particular to one kind is set using method for cleaning, device, storage medium and electronics It is standby.

Background technology

At present, on the electronic equipment such as smart mobile phone, it will usually there are multiple applications while run, wherein, one is applied preceding Platform is run, and other application is in running background.If not clearing up the application of running background for a long time, can cause electronic equipment can Diminished with internal memory, central processing unit (central processing unit, CPU) occupancy it is too high, cause electronic equipment to occur The problems such as speed of service is slack-off, interim card, and power consumption is too fast.Solved the above problems therefore, it is necessary to provide a kind of method.

The content of the invention

In view of this, the embodiment of the present application provides one kind and applies method for cleaning, device, storage medium and electronic equipment, The operation fluency of electronic equipment can be improved, reduces power consumption.

In a first aspect, one kind application method for cleaning for providing of the embodiment of the present application, including：

The multidimensional characteristic of continuous acquisition application builds multiple sample sets of the application as sample；

The sample set of predetermined number is extracted from the multiple sample set；

Based on the sample set of the predetermined number, successively according to the feature for sample classification information gain to each The sample set carries out sample classification, to construct multiple decision-tree models of the application, the output of the decision-tree model As a result include to clear up or can not clearing up；

The multidimensional characteristic applied described in collection current time is as forecast sample；

Judge whether the application can clear up according to the forecast sample and multiple decision-tree models.

Second aspect, one kind application cleaning plant for providing of the embodiment of the present application, including：

First collecting unit, the multidimensional characteristic for continuous acquisition application build the multiple of the application as sample Sample set；

Extracting unit, for extracting the sample set of predetermined number from the multiple sample set；

Construction unit, for the sample set based on the predetermined number, successively according to the feature for sample classification Information gain carries out sample classification to each sample set, described to determine to construct multiple decision-tree models of the application The output result of plan tree-model includes to clear up or can not clearing up；

Second collecting unit, for gathering the multidimensional characteristic applied described in current time as forecast sample；

Judging unit, for judging whether the application can be clear according to the forecast sample and multiple decision-tree models Reason.

The third aspect, the storage medium that the embodiment of the present application provides, is stored thereon with computer program, when the computer When program is run on computers so that the computer is performed as what the application any embodiment provided applies method for cleaning.

Fourth aspect, the electronic equipment that the embodiment of the present application provides, including processor and memory, the memory storage There is computer program, it is characterised in that the processor is by calling the computer program, for performing as the application is any What embodiment provided applies method for cleaning.

The multidimensional characteristic that the embodiment of the present application is applied by continuous acquisition is used as sample, and builds multiple samples of application Collection；The sample set of predetermined number is extracted from multiple sample sets；Sample set based on predetermined number, successively according to feature for sample The information gain of this classification carries out sample classification to each sample set, to construct multiple decision-tree models of application, decision tree The output result of model includes to clear up or can not clearing up；The multidimensional characteristic of current time application is gathered as forecast sample； Whether judge to apply according to forecast sample and multiple decision-tree models can clear up.Carried out with multiple decision-tree models synthesis using clear Reason judges, to clear up the application that can be cleared up, realizes the higher automatic cleaning of accuracy, improves the operation speed of electronic equipment Degree, and reduce power.

Brief description of the drawings

Technical scheme in order to illustrate the embodiments of the present invention more clearly, make required in being described below to embodiment Accompanying drawing is briefly described, it should be apparent that, drawings in the following description are only some embodiments of the present invention, for For those skilled in the art, on the premise of not paying creative work, it can also be obtained according to these accompanying drawings other attached Figure.

Fig. 1 is the application scenarios schematic diagram using method for cleaning that the embodiment of the present application provides.

Fig. 2 is the schematic flow sheet using method for cleaning that the embodiment of the present application provides.

Fig. 3 is a kind of schematic diagram for decision tree that the embodiment of the present application provides.

Fig. 4 is the schematic diagram for another decision tree that the embodiment of the present application provides.

Fig. 5 is the schematic diagram for another decision tree that the embodiment of the present application provides.

Fig. 6 is that the embodiment of the present application provides also a kind of schematic diagram of decision tree.

Fig. 7 is another schematic flow sheet using method for cleaning that the embodiment of the present application provides.

Fig. 8 is the structural representation using cleaning plant that the embodiment of the present application provides.

Fig. 9 is another structural representation using cleaning plant that the embodiment of the present application provides.

Figure 10 is a structural representation of the electronic equipment that the embodiment of the present application provides.

Figure 11 is another structural representation for the electronic equipment that the embodiment of the present application provides.

Embodiment

Schema is refer to, wherein identical element numbers represent identical component, and the principle of the application is to implement one Illustrated in appropriate computing environment.The following description is based on illustrated the application specific embodiment, and it should not be by It is considered as limitation the application other specific embodiments not detailed herein.

In the following description, the specific embodiment of the application is by with reference to as the step performed by one or multi-section computer And symbol illustrates, unless otherwise stating clearly.Therefore, these steps and operation will have to mention for several times is performed by computer, this paper institutes The computer of finger, which performs, to be included by representing with the computer processing unit of the electronic signal of the data in a structuring pattern Operation.The data or the opening position being maintained in the memory system of the computer are changed in this operation, and its is reconfigurable Or change the running of the computer in a manner of known to the tester of this area in addition.The data structure that the data are maintained For the provider location of the internal memory, it has the particular characteristics as defined in the data format.But the application principle is with above-mentioned text Word illustrates that it is not represented as a kind of limitation, this area tester will appreciate that plurality of step as described below and behaviour Also may be implemented among hardware.

Term as used herein " module " can regard the software object to be performed in the arithmetic system as.It is as described herein Different components, module, engine and service can be regarded as the objective for implementation in the arithmetic system.And device as described herein and side Method can be implemented in a manner of software, can also be implemented certainly on hardware, within the application protection domain.

Term " first ", " second " and " the 3rd " in the application etc. is to be used to distinguish different objects, rather than for retouching State particular order.In addition, term " comprising " and " having " and their any deformations, it is intended that cover non-exclusive include. Such as contain the step of process, method, system, product or the equipment of series of steps or module is not limited to list or Module, but some embodiments also include the step of not listing or module, or some embodiments also include for these processes, Method, product or equipment intrinsic other steps or module.

Referenced herein " embodiment " is it is meant that the special characteristic, structure or the characteristic that describe can wrap in conjunction with the embodiments It is contained at least one embodiment of the application.Each position in the description occur the phrase might not each mean it is identical Embodiment, nor the independent or alternative embodiment with other embodiments mutual exclusion.Those skilled in the art explicitly and Implicitly understand, embodiment described herein can be combined with other embodiments.

The embodiment of the present application provides one kind and applies method for cleaning, and this can be the application using the executive agent of method for cleaning What embodiment provided applies cleaning plant, or is integrated with the electronic equipment for applying cleaning plant, and the wherein application cleaning fills Putting can be realized by the way of hardware or software.Wherein, electronic equipment can be smart mobile phone, tablet personal computer, palm electricity The equipment such as brain, notebook computer or desktop computer.

Referring to Fig. 1, Fig. 1 is the application scenarios schematic diagram using method for cleaning that the embodiment of the present application provides, with application Cleaning plant it is integrated in the electronic device exemplified by, electronic equipment can be using the multidimensional characteristic that continuous acquisition is applied as sample, and structure Build multiple sample sets of the application；The sample set of predetermined number is extracted from the plurality of sample set；Sample based on the predetermined number This collection, sample classification is carried out to each sample set according to information gain of this feature for sample classification successively, to construct Multiple decision-tree models of the application, the output result of the decision-tree model include to clear up or can not clearing up；Collection is current The multidimensional characteristic of the time application is as forecast sample；Judge that the application is according to the forecast sample and multiple decision-tree models It is no to clear up.

Specifically, such as shown in Fig. 1, to judge application program a (such as social networking application, game application, the office of running background Using etc.) whether can clear up exemplified by, whether wireless network (such as can be connected using a with continuous acquisition application a multidimensional characteristic Network, temporal information etc. using a operations) be used as sample, structure using a multiple sample sets, from the plurality of sample set at random Extract the sample set of predetermined number；

Sample set based on the predetermined number, whether wireless network (such as is connected, using a using a according to this feature successively Temporal information of operation etc.) for sample classification information gain to each sample set carry out sample classification, to construct this Multiple decision-tree models of application；Multidimensional characteristic corresponding to collection current time application (such as whether connected in t application a Wireless network, temporal information etc. using a operations) it is used as forecast sample；According to the forecast sample and multiple decision-tree models Judge whether this can clear up using a.In addition, when prediction can clear up using a, electronic equipment using a to clearing up, in one kind Can be that will be closed on the backstage of electronic equipment using a to being cleared up using a in embodiment, and by using a pair The thread answered all interrupts.

Referring to Fig. 2, Fig. 2 is the schematic flow sheet using method for cleaning that the embodiment of the present application provides.The application is implemented The idiographic flow using method for cleaning that example provides can be as follows：

201st, the multidimensional characteristic of continuous acquisition application is as sample, and builds multiple sample sets of the application.

Application mentioned by the present embodiment, can be that any one installed on electronic equipment is applied, such as office application, Communications applications, game application, shopping application etc..Wherein, application can include the application of front stage operation, i.e. foreground application, also may be used With the application including running background, i.e. background application.

The multidimensional characteristic of application has a dimension of certain length, and the parameter in each of which dimension is corresponding to characterize the one of application Kind characteristic information, i.e. multidimensional characteristic breath are made up of multiple features.The plurality of feature can include the spy being associated using itself Reference ceases, such as：Using the operation duration for being cut into backstage；Using during being cut into backstage, duration is shielded in going out for electronic equipment；Should With the number for entering foreground；Using the time in foreground；Using whether connecting wireless network etc..

The plurality of characteristic information can also include the correlated characteristic information of the electronic equipment where application, such as：Electronics is set Standby go out screen time, bright screen time, current electric quantity, whether electronic equipment is in charged state etc..

Wherein, the sample set of application can include multiple samples, and each sample includes the multidimensional characteristic of application.The sample of application This concentration, it can be included in preset time threshold, the multiple samples gathered according to predeterminated frequency.In preset time threshold, example It such as can be 7 days, 14 days in the past；Predeterminated frequency, such as can be that collection in every 10 minutes once, per half an hour gathers once.Can With understanding, the multi-dimensional feature data of the application once gathered forms a sample, multiple samples, forms sample set.

After sample set is formed, each sample in sample set can be classified, obtain the sample of each sample Label, because this implementation will be accomplished that whether prediction application can clear up, therefore, the sample label classified includes clearing up With can not clear up, namely sample class include can clear up, can not clear up.History use habit that specifically can be according to user to application It is marked, such as：After application enters 30 minutes from the background, user closes the application, then is labeled as " can clear up "；Example again Such as, after application enters 3 minutes from the background, user will apply and be switched to front stage operation, then be labeled as " can not clear up ".Specifically Ground, numerical value " 1 " expression " can clear up " can be used, with numerical value " 0 " expression " can not clear up ", vice versa.

Based on this, after a sample set is formed, in preset time threshold, continue to gather multiple samples according to predeterminated frequency This, forms second sample set, by that analogy, to build multiple sample sets.It should be noted that in the plurality of sample set, bag Multidimensional characteristic containing identical.

202nd, the sample set of predetermined number is extracted from the plurality of sample set.

Wherein, in order to build multiple decision-tree models, it is necessary to randomly select out predetermined number from the plurality of sample set Sample set, to be built into decision tree forest.

In one embodiment, the sample set of predetermined number is extracted from the plurality of sample set, can be included：

(1) processing is numbered to the plurality of sample set；

Wherein it is possible to processing is numbered to multiple sample sets, to generate with numbered sample set, such as sample set 1, sample This collection 2 ... sample set n.

(2) sample set of predetermined number is randomly selected from multiple sample sets after the numbering.

Further, default is extracted from the sample set 1, sample set 2 ... sample set n according to the rule of random sampling Several sample sets, the predetermined number can be that user sets, and such as extract 3 sample sets, sample set 2, sample set 4 and sample set 7。

203rd, the sample set based on the predetermined number, successively according to this feature for sample classification information gain to each The sample set carries out sample classification, to construct multiple decision-tree models of the application, the output result bag of the decision-tree model Including can clear up or can not clear up.

In one embodiment, can be by the multidimensional characteristic information of application for ease of sample classification, the unused direct table of numerical value The characteristic information shown is come out with specific numerical quantization, such as the wireless network connection status of electronic equipment this feature letter Breath, can represent normal state with numerical value 1, the state for representing to disconnect with numerical value 0 (vice versa)；For another example it is directed to electronics Whether equipment can represent charged state with numerical value 1, uncharged state is represented with numerical value 0 in this characteristic information of charged state (vice versa).

The embodiment of the present application can the sample set based on predetermined number, the feature in each sample set is for the sample The information gain of classification carries out sample classification to each sample set, with multiple decision-tree models of structure application.Such as can be with base Decision-tree model is built in ID3 algorithms.

Wherein, decision tree is a kind of a kind of tree relied on decision-making and set up.In machine learning, decision tree is a kind of Forecast model, representative is a kind of mapping relations between a kind of object properties and object value, and some is right for each node on behalf As, each diverging paths in tree represent some possible property value, and each leaf node then correspond to from root node to The value for the object represented by path that the leaf node is undergone.Decision tree only has single output, can be with if multiple outputs Independent decision tree is established respectively to handle different output.

Wherein, ID3 (Iterative Dichotomiser 3, the generation of iteration binary tree 3) algorithm is one kind of decision tree, it It is based on "ockham's razor" principle, i.e., does more things with less thing with trying one's best.In information theory, it is expected that information is got over It is small, then information gain is bigger, so as to which purity is higher.The core concept of ID3 algorithms is exactly to be belonged to information gain to measure Property selection, the maximum attribute of information gain enters line splitting after selection division.The algorithm uses top-down greedy search time Go through possible decision space.

Wherein, information gain exactly sees a feature t for feature one by one, and system has it and do not had It when information content be respectively how many, both differences are exactly the information content that this feature is brought to system, i.e. information gain.

Therefore, different information gains is different that can be preset by setting one for the gain effect of the classification of result Threshold value, the feature that information gain is less than to the predetermined threshold value are removed, and the feature construction that the predetermined threshold value is more than with information gain is determined Plan tree-model, it is possible to reduce the operational data amount of electronic equipment, and then save the electricity of electronic equipment.

The sample set based on the predetermined number is described in detail below, successively the information according to this feature for sample classification Gain carries out sample classification to each sample set, to construct the process of multiple decision-tree models of the application, such as, including Following steps：

(1) sample set of predetermined number is obtained successively, generates corresponding root node, and using the sample set as the root node Nodal information.

(2) sample set of the root node is defined as current target sample collection to be sorted.

(3) information gain that this feature is classified for sample set in target sample collection is obtained.

(4) current division feature is chosen from this feature according to the information gain.

(5) sample set is divided according to the division feature, obtains some subsample collection.

(6) the division feature for concentrating sample to the subsample is removed, subsample collection after being removed.

(7) generate present node child node, and using this remove after subsample collection as the child node nodal information.

(8) judge whether child node meets default classification end condition.

(9) if it is not, the target sample collection then is updated into subsample collection after the removal, and return and perform step (3).

(10) if so, then using the child node as leaf node, the classification for concentrating sample according to subsample after the removal is set The output of the leaf node is put, the classification of the sample includes to clear up or can not clearing up.

Wherein, division is characterized as the feature chosen according to the information gain that each feature is classified for sample set from feature, For classifying to sample set.Wherein, according to information gain choose division feature mode have it is a variety of, such as in order to lift sample point The accuracy of class, it can choose and be characterized as dividing feature corresponding to maximum information gain.

Wherein, the classification of sample can include clearing up, can not clearing up two kinds of classifications, and the classification of each sample can use sample This mark represents, such as, when sample labeling is numerical value, numerical value " 1 " expression " can clear up " " can not be clear with numerical value " 0 " expression Reason ", vice versa.

When child node meets default classification end condition, can it stop to the son using child node as leaf node The sample set classification of node, and can concentrate the classification of sample that the output of the leaf node is set based on subsample after removal. It is a variety of that classification based on sample sets the mode of the output of leaf node to have.Such as sample size in sample set after can removing Output of most classifications as the leaf node.

Wherein, presetting classification end condition can set according to the actual requirements, and child node meets that default classification terminates bar During part, using current node as leaf node, stop carrying out participle classification to sample set corresponding to child node；Child node is not When meeting default classification end condition, continue to classify to sample set corresponding to child node.Such as default classification end condition It can include：The categorical measure of sample is and predetermined number in the set of subsample after the removal of child node, namely step " judges son Whether node meets default classification end condition " it can include：

(1) judge that subsample concentrates whether the categorical measure of sample is predetermined number after being removed corresponding to child node；

(2) if, it is determined that child node meets default classification end condition；

(3) if not, it is determined that the discontented default classified terminal end condition of child node.

For example, default classification end condition can include：The classification of sample is concentrated in subsample after being removed corresponding to child node Quantity is there was only the sample of a classification in 1, namely the sample set of child node.Now, if child node meets the default classification End condition, then, the classification of sample is concentrated into as the output of the leaf node in subsample.Subsample is concentrated only after such as removing When having the sample that classification is " can clear up ", then, can be by the output of " can clear up " as the leaf node.

In one embodiment, in order to lift the accuracy of determination of decision-tree model, a division threshold value can also be set；When When maximum information gain is more than the division threshold value, just choose the information gain for feature to divide feature.That is, step " chosen according to information gain and current division feature is chosen from feature " can include：

(1) the target information gain of maximum is chosen from information gain；

(2) judge whether target information gain is more than division threshold value；

(3) if so, then choosing feature corresponding to target information gain as current division feature.

In one embodiment, can be using present node as leaf section when target information gain is not more than predetermined threshold value Point, and choose output of the most sample class of sample size as the leaf node.Wherein, sample class include can clear up, Or it can not clear up.

Wherein, division threshold value can be set according to the actual requirements, and such as 0.3,0.4.

For example, when feature 1 for the information gain 0.7 of sample classification is maximum information gain when, division threshold value be 0.4 When, because maximum information gain is more than predetermined threshold value, at this point it is possible to using feature 1 as division feature.

In another example when it is 0.8 to divide threshold value, then maximum information gain is less than predetermined threshold value, at this point it is possible to will be current Node understands that classification is most for the sample size of " can clear up " to sample set analysis, being more than classification " can not as leaf node The sample size of cleaning ", at this point it is possible to by the output of " can clear up " as the leaf node.

Wherein, according to division feature to sample carry out classifying and dividing mode have it is a variety of, such as, can be based on division feature Characteristic value sample set divided.Namely step " being divided according to division feature to sample set " can include：

(1) characteristic value that feature is divided in sample set is obtained；

(2) sample set is divided according to characteristic value.

For example characteristic value identical sample can will be divided in sample set and is divided into same subsample concentration.For example, divide The characteristic value of feature includes：0th, 1,2, then at this point it is possible to the sample that the characteristic value for dividing feature is 0 be classified as it is a kind of, by feature The sample being worth for 1 is classified as sample that is a kind of, being 2 by characteristic value and is classified as one kind.

For example, for sample set A { sample 1, sample 2 ... sample i ... samples n }, wherein sample 1 includes feature 1, spy Sign 2 ... feature m, sample i include feature 1, feature 2 ... feature m, sample n include feature 1, feature 2 ... feature m.

First, the sample set of predetermined number is obtained, all samples in each sample set are initialized successively, then, A root node a, and the nodal information using sample set as root node a are generated, such as with reference to figure 3.

Calculate each feature information gain g1, g2 ... gm that for example feature 1, feature 2 ... feature m are classified for sample set； In gain g1, g2 ... gm, retain the gain that information gain is more than predetermined threshold value.And chosen in the gain after reservation maximum Information gain gmax, if gi is maximum information gain.

When maximum information gain gmax is less than division threshold epsilon, current node chooses sample number as leaf node Measure output of most sample class as leaf node.

When the information gain gmax of maximum is more than division threshold epsilon, feature i corresponding to information gain gmax can be chosen and made To divide feature t, sample set A { sample 1, sample 2 ... sample i ... samples n } is divided according to feature i, such as by sample This collection is divided into two sub- sample set A1 { sample 1, sample 2 ... sample k } and A2 { sample k+1 ... samples n }.

Feature t will be divided in subsample collection A1 and A2 to remove, now, in subsample collection A1 and A2 sample include feature 1, Feature 2 ... feature i-1, feature i+1 ... features n }.Generate root node a child node a1 and a2 with reference to figure 3, and by increment This collection A1 is as child node a1 nodal information, the nodal information using subsample collection A2 as child node a2.

Then, for each child node, by taking child node a1 as an example, judge whether child node meets that default classification terminates bar Part, if so, then using current child node a1 as leaf node, and the class of sample is concentrated in subsample according to corresponding to child node a1 The leaf node is not set to export.

When child node is unsatisfactory for default classification end condition, by the way of the above-mentioned classification based on information gain, continue Subsample collection corresponding to child node is classified, can such as be calculated by taking child node a2 as an example in A2 sample sets each feature relative to The information gain g of sample classification, the information gain gmax of maximum is chosen, when the information gain gmax of maximum is more than division threshold epsilon When, it can choose and be characterized as dividing feature t corresponding to information gain gmax, A2 is divided into by some sons based on division feature t Sample set, A2 can be such as divided into subsample collection A21, A22, A23, then, by the division in subsample collection A21, A22, A23 Feature t is removed, and generates present node a2 child node a21, a22, a23, will remove the sample set A21 after division feature t, The nodal information of A22, A23 respectively as child node a21, a22, a23.

The like, decision tree as shown in Figure 4 is may be constructed out using the above-mentioned mode based on information gain classification, The output of the leaf node of the decision tree includes " can clear up " or " can not clear up ".

In one embodiment, can also be among the nodes in order to lift the speed and efficiency that are predicted using decision tree Path on the corresponding division feature of mark characteristic value.Such as during the above-mentioned classification based on information gain, Ke Yi The characteristic value of present node division feature corresponding to mark on its child node path.

For example, division feature t characteristic value includes：0th, 1 when, mark 1 on path that can be between a2 and a, in a1 and a Between path on mark 0, the like, can be with the path subscript of present node and its child node after each division The corresponding division characteristic value such as 0 or 1 of note, can be to obtain decision tree as shown in Figure 5.

The like, all samples in each sample set are initialized successively according to the method described above, constructed multiple Decision-tree model, to generate decision tree forest, as shown in fig. 6, the two decision tree moulds built for sample set 1 and sample set 2 Type.

In the embodiment of the present application, the bar of empirical entropy that can be based on sample classification and feature for sample set classification results Part entropy, obtain the information gain that feature is classified for sample set.Namely " feature is for sample set in acquisition target sample collection for step The information gain of classification " can include：

(1) empirical entropy of sample classification is obtained；

(2) conditional entropy of the feature for sample set classification results is obtained；

(3) according to conditional entropy and empirical entropy, the information gain that feature is classified for sample set is obtained.

Wherein it is possible to obtain the first probability that positive sample occurs in sample set and negative sample occurs in sample set The second probability, positive sample is that sample class is the sample that can clear up, and negative sample is that sample class is the sample that can not be cleared up；Root The empirical entropy of sample is obtained according to the first probability and the second probability.

For example, for sample set Y { sample 1, sample 2 ... sample i ... samples n }, if sample class is to clear up Sample size be j, the sample size that can not be cleared up is n-j；Now, probability of occurrence p1=j/ of the positive sample in sample set Y N, probability of occurrence p2=n-j/n of the negative sample in sample set Y.Then, the calculation formula based on following empirical entropy, calculates sample The empirical entropy H (Y) of this classification：

Wherein, pi is probability of occurrence of the sample in sample set Y.In decision tree classification problem, information gain is exactly certainly The difference of plan tree information before Attributions selection division is carried out and after division.

In one embodiment, sample set can be divided into by some subsample collection according to feature t, then, obtains each increment The comentropy of this collection classification, and this feature t probability that occurs in sample set of each characteristic value, according to the comentropy and should Probability can be with the comentropy after being divided, i.e. conditional entropies of this feature t for sample set classification results.

For example, for sample characteristics X, sample characteristics X can be by following for the conditional entropy of sample set Y classification results Formula is calculated：

Wherein, n is characterized X value kind number, i.e. characteristic value number of types.Now, pi is that X characteristic values are i-th kind of value The probability that occurs in sample set Y of sample, xi is X i-th kind of value.H (Y | X=xi) it is the experience that subsample collection Yi classifies Entropy, the X characteristic values of sample are i-th kind of value in the collection i of the subsample.

For example, using feature X value kind number as 3, i.e., exemplified by x1, x2, x3, at this point it is possible to which feature X is by sample set Y { samples 1st, sample 2 ... sample i ... samples n } three sub- sample sets are divided into, characteristic value is x1 Y1 { sample 1, sample 2 ... sample This d }, the Y2 { sample d+1 ... samples e } that characteristic value is x2, the Y3 { sample e+1 ... samples n } that characteristic value is x3.D, e is equal For positive integer, and it is less than n.

Now, feature X is for the conditional entropy of sample set Y classification results：

H (Y | X)=p1H (Y | x1)+p2H (Y | x2)+p3H (Y | x3)；

Wherein, p1=Y1/Y, p2=Y2/Y, p2=Y3/Y；

H (Y | x1) it is the comentropy that subsample collection Y1 classifies, i.e. empirical entropy, the calculation formula of above-mentioned empirical entropy can be passed through It is calculated.

Obtaining the empirical entropy H (Y) of sample classification, and feature X is for the conditional entropy H (Y | X) of sample set Y classification results Afterwards, can be such as calculated with calculating the information gain that feature X classifies for sample set Y by below equation：

G (Y, X)=H (Y)-H (Y | X)

Namely feature X is for the sample set Y information gains classified：Empirical entropy H (Y) and feature X classifies for sample set Y As a result conditional entropy H (Y | X) difference.

204th, the multidimensional characteristic of the collection current time application is as forecast sample.

Wherein it is possible to current point in time acquisition applications multidimensional characteristic as forecast sample.

In the embodiment of the present application, the multidimensional characteristic gathered in step 201 and 204 is same characteristic features, such as：Using whether connecting Connect wireless network；Using during being cut into backstage, duration is shielded in going out for electronic equipment；Using the number for entering foreground；Using in The time on foreground；Using the mode for entering backstage.

205th, judge whether the application can clear up according to the forecast sample and multiple decision-tree models.

Specifically, corresponding multiple output results are obtained according to forecast sample and multiple decision-tree models, according to multiple defeated Go out result and determine whether application can clear up.Wherein, output result includes to clear up or can not clearing up.

For example corresponding leaf node can be determined successively according to the feature and each decision-tree model of forecast sample, will The output of the leaf node is as prediction output result.Such as using forecast sample feature according to decision tree branch condition (i.e. Divide the characteristic value of feature) current leaf node is determined, take result of the output of the leaf node as prediction.Due to leaf Whether the output of node includes to clear up or can not clearing up, therefore, be able to can now be cleared up based on decision tree to determine to apply.

For example, after the multidimensional characteristic of collection current point in time application, can be in the decision tree shown in Fig. 5 according to decision tree Branch condition to search corresponding leaf node be an1, leaf node an1 output is can clear up, now, just determine to apply is It can clear up.

Based on this, multiple output results corresponding to multiple decision trees can be obtained.Analyze the plurality of output result, according to than Whether the higher output result of example can clear up to determine to apply.Such as, when multiple output results for that can clear up, can not clear up, can be clear When managing, can clear up, cleared up according to ratio is higher to determine to apply as that can clear up.

From the foregoing, it will be observed that the multidimensional characteristic that the embodiment of the present application is applied by continuous acquisition is used as sample, and build application Multiple sample sets；The sample set of predetermined number is extracted from multiple sample sets；Sample set based on predetermined number, successively according to spy The information gain levied for sample classification carries out sample classification to each sample set, to construct multiple decision tree moulds of application Type, the output result of decision-tree model include to clear up or can not clearing up；Gather the multidimensional characteristic conduct of current time application Forecast sample；Whether judge to apply according to forecast sample and multiple decision-tree models can clear up.Integrated with multiple decision-tree models Judge using cleaning, to clear up the application that can be cleared up, realize the higher automatic cleaning of accuracy, improve electronic equipment The speed of service, and reduce power.

Further, due in each sample of each sample set, including behavioural habits of the reflection user using application Multiple characteristic informations, therefore the embodiment of the present application can make it that the cleaning to corresponding application is more personalized and intelligent.

Further, realized based on multiple decision tree forecast models using cleaning prediction, multiple decision-makings are formed one Random forest, and be all not have independently related between multiple decision trees, each decision tree is compareed with forecast sample, obtained more Individual output result, using output result ratio highest output result as application whether the basis for estimation that can be cleared up, can improve The accuracy rate of user's behavior prediction, and then improve the degree of accuracy of cleaning.

On the basis of the method that will be described below in above-described embodiment, the method for cleaning of the application is described further.Ginseng Fig. 7 is examined, this can include using method for cleaning：

301st, the multidimensional characteristic of continuous acquisition application is as sample, and builds multiple sample sets of application, in sample set Sample classified, obtain the sample label of each sample.

The multidimensional characteristic information of application has a dimension of certain length, and the parameter in each of which dimension is corresponding to characterize application A kind of characteristic information, i.e. the multidimensional characteristic information is made up of multiple characteristic informations.The plurality of characteristic information can include application Itself related characteristic information, such as：Using the duration for being cut into backstage；Using during being cut into backstage, electronic equipment goes out Shield duration；Using the number for entering foreground；Using the time in foreground；Using the mode for entering backstage, such as by homepage key (home keys), which switch into, is returned key switches into, and is switched into by other application；The type of application, including one-level is (often With application), two level (other application) etc..The plurality of characteristic information can also include the correlated characteristic of the electronic equipment where application Information, such as：Go out screen time, bright screen time, the current electric quantity of electronic equipment, the wireless network connection status of electronic equipment, electricity Whether sub- equipment is in charged state etc..

In the sample set of application, it can be included in preset time threshold, the multiple samples gathered according to predeterminated frequency.In advance If in time threshold, such as it can be 7 days, 14 days in the past；Predeterminated frequency, such as can gather once for every 10 minutes, per half Hour collection is once.It is understood that the multi-dimensional feature data of an acquisition applications forms a sample, multiple samples, structure Into sample set.

Further, after a sample set is formed, in historical time section, continue to gather multiple samples according to predeterminated frequency This, forms second sample set, by that analogy, to build multiple sample sets.It should be noted that in the plurality of sample set, bag Multidimensional characteristic containing identical.

In one embodiment, after multiple sample sets are built, a preset time threshold can also be set, when there is sample set Generation time when exceeding the preset time threshold, illustrate that the sample set existence time is longer, use that may be current with user Custom is not adapted to, and the sample set is deleted, and in preset time threshold, continues to gather multiple samples according to predeterminated frequency, with structure New sample set is built, with this, keeps the instantaneity of sample set, to ensure that the current use of the sample application user in sample set is practised It is used.

One specific sample can be as shown in table 1 below, including multiple dimensions characteristic information, it is necessary to explanation, the institute of table 1 The characteristic information shown is only for example, and in practice, the quantity for the characteristic information that a sample is included, can be more than than shown in table 1 The quantity of information, the quantity of information shown in table 1 can also be less than, the specific features information taken can also be different from shown in table 1, It is not especially limited herein.

Table 1

Because this implementation will be accomplished that whether prediction application can clear up, therefore, the sample label marked includes can Clear up and can not clear up.The sample label of the sample characterizes the sample class of the sample.Now, sample class can include can be clear Reason, it can not clear up.

In addition, the history use habit of application can be also marked according to user, such as：When application enters 30 points of backstage Zhong Hou, user close the application, then are labeled as " can clear up "；For another example after using entering 3 minutes from the background, user will Using front stage operation has been switched to, then " can not clear up " is labeled as.Specifically, numerical value " 1 " expression " can clear up " can be used, uses number Value " 0 " expression " can not clear up ", vice versa.

302nd, processing is numbered to multiple sample sets.

Wherein it is possible to processing is numbered to multiple sample sets, to generate with numbered sample set, such as sample set 1, sample This collection 2 ... sample set n.Include the same multidimensional characteristic in each sample set, sample size can be it is the same or different, It is not especially limited herein.

303rd, the sample set of predetermined number is randomly selected from multiple sample sets after numbering.

Wherein, predetermined number is extracted from the sample set 1, sample set 2 ... sample set n according to the rule of random sampling Sample set, the predetermined number can be that user sets, and such as extract 3 sample sets, sample set 1, sample set 3 and sample set 5.

304th, the sample set of predetermined number is obtained successively, generates corresponding root node, and using sample set as root node Nodal information.

Wherein, each sample set in the sample set of predetermined number is obtained successively, such as, with reference to figure 3, first obtain sample set A, for sample set A { sample 1, sample 2 ... sample i ... samples n }, it can first generate the root node a of decision tree, and by sample Nodal informations of this collection A as root node a.

305th, the sample set of root node is defined as current target sample collection to be sorted.

Determine the sample set of root node as current target sample collection to be sorted.

306th, the information gain that feature is classified for sample set in target sample collection is obtained.

For example for sample set A, each feature can be calculated as feature 1, feature 2 ... feature m classify for sample set Information gain g1, g2 ... gm；Choose maximum information gain gmax.

Wherein, the information gain that feature is classified for sample set, can obtain in the following way：

Obtain the empirical entropy of sample classification；Obtain conditional entropy of the feature for sample set classification results；According to conditional entropy and Empirical entropy, obtain the information gain that feature is classified for sample set.

For example the first probability that positive sample occurs in sample set can be obtained and negative sample occurs in sample set The second probability, positive sample is that sample class is the sample that can clear up, and negative sample is that sample class is the sample that can not be cleared up；Root The empirical entropy of sample is obtained according to the first probability and the second probability.

In decision tree classification problem, information gain is exactly decision tree information after carrying out Attributions selection and dividing preceding and division Difference.

H (Y | X)=p1H (Y | x1)+p2H (Y | x2)+p3H (Y | x3)；

Wherein, p1=Y1/Y, p2=Y2/Y, p2=Y3/Y；

G (Y, X)=H (Y)-H (Y | X)

307th, the target information gain of maximum is chosen from information gain.

308th, judge whether target information gain is more than division threshold value.

Wherein, when judging that target information gain is more than division threshold value, step 309 is performed；When judging target information When gain is more than division threshold value, step 310 is performed.Such as, it can be determined that it is default whether maximum information gain gmax is more than Threshold epsilon, the threshold epsilon can be set according to the actual requirements.

309th, feature corresponding to target information gain is chosen as current division feature, and according to division feature to sample Collection is divided, and obtains some subsample collection.

Such as when feature corresponding to the information gain gmax of maximum is characterized i, can be using selected characteristic i as division feature.

Specifically, sample set can be divided into by some subsample collection, subsample according to the characteristic value kind number of division feature The quantity of collection is identical with characteristic value kind number.For example, same son can be divided into by characteristic value identical sample is divided in sample set In sample set.For example, dividing the characteristic value of feature includes：0th, 1,2, then at this point it is possible to the sample that the characteristic value for dividing feature is 0 Originally it is classified as sample that is a kind of, being 1 by characteristic value and is classified as sample that is a kind of, being 2 by characteristic value being classified as one kind.

310th, using present node as leaf node, and the most sample class of sample size is chosen as leaf node Output.

Wherein, sample class includes to clear up, can not clearing up.

For example, in child node a1 subsample collection A1 classification, if maximum information gain is small and predetermined threshold value, now, Output that can be using the most sample class of sample size in the collection A1 of subsample as the leaf node.Such as the sample of " can not clear up " This quantity is most, then can be by the output of " can not clear up " as leaf node a1.

311st, the division feature of sample in sub- sample set is removed, subsample collection after being removed.

For example sample set A can be divided into A1 { sample 1, sample 2 ... sample when having two kinds by division feature i value This k } and A2 { sample k+1 ... samples n }.It is then possible to the division feature i in subsample collection A1 and A2 is removed.

312nd, the child node of present node is generated, and using subsample collection after removal as the nodal information of child node.

Wherein, the corresponding child node of a sub- sample set.For example, Fig. 3 generation root nodes a child node a1 and a2 is examined, And using subsample collection A1 as child node a1 nodal information, the nodal information using subsample collection A2 as child node a2.

313rd, judge whether child node meets default classification end condition.

Wherein, when judging that child node meets default classification end condition, step 314 is performed；When judging child node When being unsatisfactory for default classification end condition, step 315 is performed.

Wherein, presetting classification end condition can set according to the actual requirements, and child node meets that default classification terminates bar During part, using current node as leaf node, stop carrying out participle classification to sample set corresponding to child node；Child node is not When meeting default classification end condition, continue to classify to sample set corresponding to child node.Such as default classification end condition It can include：The categorical measure of sample is and predetermined number in the set of subsample after the removal of child node.

For example, default classification end condition can include：The classification of sample is concentrated in subsample after being removed corresponding to child node Quantity is there was only the sample of a classification in 1, namely the sample set of child node.

314th, using child node as leaf node, the classification for concentrating sample according to subsample after removal sets leaf node Output.

Now, if child node meets the default classification end condition, then, using subsample concentrate the classification of sample as The output of the leaf node.When the sample for only having classification to be " can clear up " is concentrated in subsample after such as removing, then, can will " can Output of the cleaning " as the leaf node.

315th, target sample collection is updated to subsample collection after removing, and returns and perform step 306.

316th, after multiple decision-tree models have been built, the multidimensional characteristic of collection current time application is as pre- test sample.

Wherein, the multidimensional characteristic of current time application is identical with the multidimensional characteristic of sample in sample set.

317th, judged one by one according to forecast sample and each decision tree, determine multiple corresponding output results.

For example corresponding leaf node can be determined according to the feature and each decision-tree model of forecast sample, by the leaf The output of child node is as prediction output result.Such as (divided according to the branch condition of decision tree using the feature of forecast sample The characteristic value of feature) current leaf node is determined, take result of the output of the leaf node as prediction.Due to leaf node Output include can clear up or can not clear up, therefore, now can be based on decision tree come determine apply whether can clear up.

For example, after the multidimensional characteristic of collection current point in time application, can be in the decision tree shown in Fig. 5 according to decision tree Branch condition to search corresponding leaf node be an2, leaf node an2 output is to clear up, now, just output result It is to clear up.

It is determined that after a complete decision-tree model, next decision-tree model is determined, by that analogy, it may be determined that go out more Individual corresponding output result, such as 5 output results, can respectively clear up, can not clear up, can clear up, can not clear up and can be clear Reason.

318th, multiple output results are analyzed, determine whether the application can clear up according to the higher output result of ratio.

Wherein, after the plurality of output result is obtained, it can such as clear up, can not clear up, can clear up, can not clear up and can be clear Reason, the application is determined according to the higher output result of ratio " can clear up " for application can be cleared up, it is corresponding to apply this from backstage Close, and by this apply corresponding to thread kill.

One kind is additionally provided in one embodiment applies cleaning plant.Referring to Fig. 8, Fig. 8 provides for the embodiment of the present application The structural representation using cleaning plant.Wherein this is applied to electronic equipment using cleaning plant, and this applies cleaning plant bag The first collecting unit 401, extracting unit 402, construction unit 403, the second collecting unit 404 and judging unit 405 are included, it is as follows：

First collecting unit 401, the multidimensional characteristic for continuous acquisition application build the multiple of the application as sample Sample set.

Extracting unit 402, for extracting the sample set of predetermined number from the plurality of sample set.

Construction unit 403, for the sample set based on the predetermined number, the letter according to this feature for sample classification successively Cease gain and sample classification is carried out to each sample set, to construct multiple decision-tree models of the application, the decision-tree model Output result include can clear up or can not clear up.

Second collecting unit 404, for gathering the multidimensional characteristic of the current time application as forecast sample.

Judging unit 405, for judging whether the application can clear up according to the forecast sample and multiple decision-tree models.

In one embodiment, with reference to figure 9, construction unit 403, can include：

First generation subelement 4031, for obtaining the sample set of the predetermined number successively, generates corresponding root node, and Nodal information using the sample set as the root node；The sample set of the root node is defined as current target sample to be sorted Collection.

Gain obtains subelement 4032, increases for obtaining this feature in target sample collection for the information that sample set is classified Benefit.

Subelement 4033 is chosen, in choosing current division feature from this feature according to the information gain.

Subelement 4034 is divided, for being divided according to the division feature to the sample set, obtains some subsample collection.

Second generation subelement 4035, concentrates the division feature of sample to be removed the subsample, obtains for removing Subsample collection after to removal；Generate present node child node, and using this remove after subsample collection as the child node node Information.

Judgment sub-unit 4036, for judging whether child node meets default classification end condition；If it is not, then by the target Sample set is updated to subsample collection after the removal, and returns to perform and obtain what this feature in target sample collection was classified for sample set The step of information gain；If so, then using the child node as leaf node, according to the classification of concentration sample in subsample after the removal The output of the leaf node is set, and the classification of the sample includes to clear up or can not clearing up.

Wherein, the selection subelement 4033, can be used for：The target information gain of maximum is chosen from the information gain.

Judge whether the target information gain is more than division threshold value.

If so, feature corresponding to the target information gain is then chosen as current division feature.

Wherein, the gain obtains subelement 4032, can be used for：Obtain the empirical entropy of sample classification.

Obtain conditional entropy of this feature for sample set classification results.

According to the conditional entropy and the empirical entropy, the information gain that this feature is classified for the sample set is obtained.

In one embodiment, with reference to figure 9, judging unit 405, can include：

First determination subelement 4051, for being judged one by one according to the forecast sample and each decision tree, it is determined that Go out multiple corresponding output results.

Second determination subelement 4052, for analyzing the plurality of output result, according to the higher output result of ratio come really Whether the fixed application can clear up.

Wherein, the method that the step of being performed using each unit in cleaning plant may be referred to the description of above method embodiment walks Suddenly.This can be integrated in the electronic device using cleaning plant, such as mobile phone, tablet personal computer.

When it is implemented, above unit can be realized as independent entity, can also be combined, as Same or several entities realize that the specific implementation of the above each unit can be found in embodiment above, will not be repeated here.

From the foregoing, it will be observed that the multidimensional characteristic that the embodiment of the present application is applied by the continuous acquisition of the first collecting unit 401 is used as sample This, and build multiple sample sets of application；Extracting unit 402 extracts the sample set of predetermined number from multiple sample sets；Structure Sample set of the unit 403 based on predetermined number, each sample set is entered according to information gain of the feature for sample classification successively Row sample classification, to construct multiple decision-tree models of application, the output result of decision-tree model includes clearing up or not It can clear up；The multidimensional characteristic of second collecting unit 404 collection current time application is as forecast sample；The basis of judging unit 405 Whether forecast sample and multiple decision-tree models judge to apply can clear up.With multiple decision-tree models synthesis using cleaning sentence It is disconnected, to clear up the application that can be cleared up, the higher automatic cleaning of accuracy is realized, improves the speed of service of electronic equipment, and Reduce power.

The embodiment of the present application also provides a kind of electronic equipment.Referring to Fig. 10, electronic equipment 500 include processor 501 with And memory 502.Wherein, processor 501 is electrically connected with memory 502.

The processor 500 is the control centre of electronic equipment 500, utilizes various interfaces and the whole electronic equipment of connection Various pieces, the computer program in memory 502 by operation or load store, and call and be stored in memory Data in 502, the various functions and processing data of electronic equipment 500 are performed, so as to carry out overall prison to electronic equipment 500 Control.

The memory 502 can be used for storage software program and module, and processor 501 is stored in memory by operation 502 computer program and module, so as to perform various function application and data processing.Memory 502 can mainly include Storing program area and storage data field, wherein, storing program area can storage program area, the computer needed at least one function Program (such as sound-playing function, image player function etc.) etc.；Storage data field can store uses institute according to electronic equipment Data of establishment etc..In addition, memory 502 can include high-speed random access memory, non-volatile memories can also be included Device, for example, at least a disk memory, flush memory device or other volatile solid-state parts.Correspondingly, memory 502 can also include Memory Controller, to provide access of the processor 501 to memory 502.

In the embodiment of the present application, the processor 501 in electronic equipment 500 can be according to the steps, by one or one Instruction is loaded into memory 502 corresponding to the process of computer program more than individual, and is stored in by the operation of processor 501 Computer program in reservoir 502, it is as follows so as to realize various functions：

The sample set of predetermined number is extracted from the plurality of sample set；

Sample set based on the predetermined number, successively according to this feature for sample classification information gain to each sample This collection carries out sample classification, and to construct multiple decision-tree models of the application, the output result of the decision-tree model includes can Clear up or can not clear up；

The multidimensional characteristic of the collection current time application is as forecast sample；

In some embodiments, judge whether the application can clear up according to the forecast sample and multiple decision-tree models When, processor 501 can specifically perform following steps：

Judged one by one according to the forecast sample and each decision tree, determine multiple corresponding output results；

The plurality of output result is analyzed, determines whether the application can clear up according to the higher output result of ratio.

In some embodiments, when the sample set of predetermined number is extracted from the plurality of sample set, processor 501 can be with It is specific to perform following steps：

Processing is numbered to the plurality of sample set；

The sample set of predetermined number is randomly selected from multiple sample sets after the numbering.

In some embodiments, the sample set based on the predetermined number, successively according to this feature for sample classification Information gain carries out sample classification, during constructing multiple decision-tree models of the application, processor 501 to each sample set Following steps can specifically be performed：

The sample set of the predetermined number is obtained successively, generates corresponding root node, and using the sample set as the root node Nodal information；

The sample set of the root node is defined as current target sample collection to be sorted；

Obtain the information gain that this feature is classified for sample set in target sample collection；

Current division feature is chosen from this feature according to the information gain；

The sample set is divided according to the division feature, obtains some subsample collection；

The division feature for concentrating sample to the subsample is removed, subsample collection after being removed；

Generate present node child node, and using this remove after subsample collection as the child node nodal information；

Judge whether child node meets default classification end condition；

If it is not, the target sample collection then is updated into subsample collection after the removal, and returns to execution and obtain target sample collection The step of information gain that interior this feature is classified for sample set；

If so, then using the child node as leaf node, concentrating the classification of sample to set according to subsample after the removal should The output of leaf node, the classification of the sample include to clear up or can not clearing up.

In some embodiments, it is more than according to the information gain and current division spy is chosen in this feature of predetermined threshold value During sign, processor 501 can specifically perform following steps：

The target information gain of maximum is chosen from the information gain；

Judge whether the target information gain is more than division threshold value；

In some embodiments, processor 501 can also specifically perform following steps：

When the target information gain is not more than predetermined threshold value, using present node as leaf node, and sample number is chosen Measure output of most sample class as the leaf node.

In some embodiments, when judging whether child node meets default classification end condition, processor 501 can have Body performs following steps：

Judge that subsample concentrates whether the categorical measure of sample is predetermined number after being removed corresponding to the child node；

If, it is determined that the child node meets default classification end condition.

In some embodiments, when obtaining the information gain that this feature is classified for sample set in target sample collection, place Reason device 501 can specifically perform following steps：

Obtain the empirical entropy of sample classification；

Obtain conditional entropy of this feature for sample set classification results；

From the foregoing, the electronic equipment of the embodiment of the present application, the multidimensional characteristic applied by continuous acquisition is used as sample, And build multiple sample sets of application；The sample set of predetermined number is extracted from multiple sample sets；Sample based on predetermined number Collection, sample classification is carried out to each sample set according to information gain of the feature for sample classification successively, to construct application Multiple decision-tree models, the output result of decision-tree model include to clear up or can not clearing up；Gather current time application Multidimensional characteristic is as forecast sample；Whether judge to apply according to forecast sample and multiple decision-tree models can clear up.Determined with multiple Plan tree-model synthesis judge using cleaning, to clear up the application that can be cleared up, realizes the higher automatic cleaning of accuracy, carry The high speed of service of electronic equipment, and reduce power.

Also referring to Figure 11, in some embodiments, electronic equipment 500 can also include：Display 503, radio frequency Circuit 504, voicefrequency circuit 505 and power supply 506.Wherein, wherein, display 503, radio circuit 504, voicefrequency circuit 505 with And power supply 506 is electrically connected with processor 501 respectively.

The display 503 is displayed for the information inputted by user or is supplied to the information of user and various figures User interface, these graphical user interface can be made up of figure, text, icon, video and its any combination.Display 503 can include display panel, in some embodiments, can use liquid crystal display (Liquid Crystal Display, LCD) or the form such as Organic Light Emitting Diode (Organic Light-Emitting Diode, OLED) match somebody with somebody Put display panel.

The radio circuit 504 can be used for transceiving radio frequency signal, to pass through radio communication and the network equipment or other electronics Equipment establishes wireless telecommunications, the receiving and transmitting signal between the network equipment or other electronic equipments.

The voicefrequency circuit 505 can be used for providing the audio between user and electronic equipment by loudspeaker, microphone and connecing Mouthful.

The power supply 506 is used to all parts power supply of electronic equipment 500.In certain embodiments, power supply 506 can With logically contiguous by power-supply management system and processor 501, so that charged, discharged by power-supply management system realization management, And the function such as power managed.

Although not shown in Figure 11, electronic equipment 500 can also include camera, bluetooth module etc., will not be repeated here.

The embodiment of the present application also provides a kind of storage medium, and the storage medium is stored with computer program, when the computer When program is run on computers so that the computer performs the method for cleaning of applying in any of the above-described embodiment, such as：Continuously The multidimensional characteristic of acquisition applications builds multiple sample sets of application as sample；Predetermined number is extracted from multiple sample sets Sample set；Sample set based on predetermined number, successively according to feature for sample classification information gain to each sample set Carry out sample classification, to construct multiple decision-tree models of application, the output result of decision-tree model include clearing up or It can not clear up；The multidimensional characteristic of current time application is gathered as forecast sample；According to forecast sample and multiple decision-tree models Whether judge to apply can clear up.With multiple decision-tree models synthesis using cleaning judge, it is real to clear up the application that can be cleared up Show the higher automatic cleaning of accuracy, improve the speed of service of electronic equipment, and reduce power.

In the embodiment of the present application, storage medium can be magnetic disc, CD, read-only storage (Read Only Memory, ROM) or random access memory (Random Access Memory, RAM) etc..

In the above-described embodiments, the description to each embodiment all emphasizes particularly on different fields, and does not have the portion being described in detail in some embodiment Point, it may refer to the associated description of other embodiment.

It should be noted that for application method for cleaning to the embodiment of the present application, this area common test personnel can be with Understand all or part of flow using method for cleaning for realizing the embodiment of the present application, be that can be controlled by computer program Related hardware is completed, and the computer program can be stored in a computer read/write memory medium, be such as stored in electronics In the memory of equipment, and by least one computing device in the electronic equipment, it may include in the process of implementation such as application The flow of the embodiment of method for cleaning.Wherein, described storage medium can be magnetic disc, CD, read-only storage, arbitrary access note Recall body etc..

For application cleaning plant to the embodiment of the present application, its each functional module can be integrated in a process chip In or modules be individually physically present, can also two or more modules be integrated in a module.It is above-mentioned Integrated module can both be realized in the form of hardware, can also be realized in the form of software function module.It is described integrated If module realized in the form of software function module and as independent production marketing or in use, one can also be stored in In individual computer read/write memory medium, the storage medium is for example read-only storage, disk or CD etc..

One kind application method for cleaning, device, storage medium and the electronic equipment provided above the embodiment of the present application enters Go and be discussed in detail, specific case used herein is set forth to the principle and embodiment of the application, and the above is implemented The explanation of example is only intended to help and understands the present processes and its core concept；Meanwhile for those skilled in the art, according to According to the thought of the application, there will be changes in specific embodiments and applications, in summary, this specification content It should not be construed as the limitation to the application.

Claims

1. one kind applies method for cleaning, it is characterised in that including：

Based on the sample set of the predetermined number, successively according to the feature for sample classification information gain to each described Sample set carries out sample classification, to construct multiple decision-tree models of the application, the output result of the decision-tree model Including that can clear up or can not clear up；

2. apply method for cleaning as claimed in claim 1, it is characterised in that described according to the forecast sample and multiple described Decision-tree model judges whether the application can clear up, including：

The multiple output result is analyzed, determines whether the application can clear up according to the higher output result of ratio.

3. apply method for cleaning as claimed in claim 1, it is characterised in that described extracted from the multiple sample set is preset The sample set of number, including：

Processing is numbered to the multiple sample set；

4. apply method for cleaning as claimed in claim 3, it is characterised in that the sample set based on the predetermined number, Sample classification is carried out to each sample set according to information gain of the feature for sample classification successively, to construct Multiple decision-tree models of application are stated, including：

The sample set of the predetermined number is obtained successively, generates corresponding root node, and using the sample set as described section The nodal information of point；

Obtain the information gain that the feature is classified for sample set in target sample collection；

Current division feature is chosen from the feature according to described information gain；

Generate the child node of present node, and the nodal information using subsample collection after the removal as the child node；

Judge whether child node meets default classification end condition；

If it is not, the target sample collection then is updated into subsample collection after the removal, and returns to execution and obtain target sample collection The step of information gain that the interior feature is classified for sample set；

If so, then using the child node as leaf node, the classification for concentrating sample according to subsample after the removal sets institute The output of leaf node is stated, the classification of the sample includes to clear up or can not clearing up.

5. apply method for cleaning as claimed in claim 4, it is characterised in that selected from the feature according to described information gain Current division feature is taken, including：

The target information gain of maximum is chosen from described information gain；

6. apply method for cleaning as claimed in claim 5, it is characterised in that the application method for cleaning also includes：

When the target information gain is no more than division threshold value, using present node as leaf node, and sample size is chosen Output of most sample class as the leaf node.

7. apply method for cleaning as claimed in claim 4, it is characterised in that judge whether child node meets that default classification terminates Condition, including：

8. apply method for cleaning as described in claim any one of 4-7, it is characterised in that institute in the acquisition target sample collection The information gain that feature is classified for sample set is stated, including：

Obtain the empirical entropy of sample classification；

Obtain conditional entropy of the feature for sample set classification results；

According to the conditional entropy and the empirical entropy, the information gain that the feature is classified for the sample set is obtained.

9. apply method for cleaning as claimed in claim 8, it is characterised in that according to the conditional entropy and the empirical entropy, obtain The information gain for taking the feature to classify for the sample set, including：

G (Y, X)=H (Y)-H (Y | X)

Wherein, g (Y, X) is characterized the information gain that X classifies for sample set Y, the empirical entropy that H (Y) classifies for sample set Y, H (Y | X) it is characterized conditional entropies of the X for sample set Y classification results.

10. one kind applies cleaning plant, it is characterised in that including：

First collecting unit, the multidimensional characteristic for continuous acquisition application build multiple samples of the application as sample Collection；

Construction unit, for the sample set based on the predetermined number, the information according to the feature for sample classification successively Gain carries out sample classification to each sample set, to construct multiple decision-tree models of the application, the decision tree The output result of model includes to clear up or can not clearing up；

Judging unit, for judging whether the application can clear up according to the forecast sample and multiple decision-tree models.

11. apply cleaning plant as claimed in claim 10, it is characterised in that the judging unit includes：

First determination subelement, for being judged one by one according to the forecast sample and each decision tree, determine more Individual corresponding output result；

Second determination subelement, for analyzing the multiple output result, determined according to the higher output result of ratio described in Using whether can clearing up.

12. apply cleaning plant as claimed in claim 10, it is characterised in that the construction unit includes：

First generation subelement, for obtaining the sample set of the predetermined number successively, corresponding root node is generated, and by described in Nodal information of the sample set as the root node；The sample set of the root node is defined as current target sample to be sorted Collection；

Gain obtains subelement, for obtaining the information gain that the feature is classified for sample set in target sample collection；

Subelement is chosen, for choosing current division feature from the feature according to described information gain；

Subelement is divided, for being divided according to the division feature to the sample set, obtains some subsample collection；

Second generation subelement, for concentrating the division feature of sample to be removed to the subsample, after obtaining removal Subsample collection；Generate the child node of present node, and the nodal information using subsample collection after the removal as the child node；

Judgment sub-unit, for judging whether child node meets default classification end condition；If it is not, then by the target sample collection Subsample collection after the removal is updated to, and returns to the letter for performing and obtaining that the feature is classified for sample set in target sample collection The step of ceasing gain；If so, then using the child node as leaf node, according to the class of concentration sample in subsample after the removal The output of the leaf node is not set, and the classification of the sample includes to clear up or can not clearing up.

13. apply cleaning plant as claimed in claim 12, it is characterised in that the selection subelement, be used for：

14. cleaning plant is applied as claimed in claim 11, it is characterised in that the gain obtains subelement, is used for：

Obtain the empirical entropy of sample classification；

15. a kind of storage medium, is stored thereon with computer program, it is characterised in that when the computer program is in computer During upper operation so that the computer performs applies method for cleaning as described in any one of claim 1 to 9.

16. a kind of electronic equipment, including processor and memory, the memory storage have computer program, it is characterised in that The processor applies cleaning side by calling the computer program, for performing as described in any one of claim 1 to 9 Method.